TRANSLATION BY QUASI LOGICAL FORM TRANSFER Hiyan Alshawi, David Carter and Manny P~yner SRI International Cambridge Computer Science Research Centre 23 Millers Yard, Cambridge CB2 1RQ, U.K. hiyan@cam, sri. com, dmc@cam, sri. com, manny¢cam, sri. com BjSrn Gambiick Swedish Institute of Computer Science Box 1263, S- 164 28 KISTA, Stockholm gain@sits, se ABSTRACT The paper describes work on applying a gen- eral purpose natural language processing system to transfer-based interactive translation. Trans- fer takes place at the level of Quasi Logical Form (QLF), a contextually sensitive logical form rep- resentation which is deep enough for dealing with cross-linguistic differences. Theoretical arguments and experimental results are presented to support the claim that this framework has good proper- ties in terms of modularity, compositionality, re- versibility and monotonicity. 1 INTRODUCTION In this paper we describe a translation project whose aim is to build an experimental Bilingual Conversation Interpreter (BCI) which will allow communication through typed text between two monolingual humans using different languages (of Miike et al, 1988). The choice of languages for the prototype system is English and Swedish. Input sentences are analysed by the Core Language En- gine (CLE 1) as far as the level of Quasi Logical Form (QLF; Alshawi, 1990), and then, instead of further ambiguity resolution, undergo transfer into another QLF having constants and predicates cor- responding to word senses in the other language. The transfer rules used in this process correspond to a certain kind of meaning postulate. The CLE then generates an output, sentence from the target 1 Tile CLE is described in Alshawi (1991) which includes more detailed discussion of the BCI architecture in a chap- ter by the present, authors, language QLF, using the same linguistic data as is used for analysis of that language. QLFs were selected as the appropriate level for transfer because they are far enough removed from surface linguistic form to provide the flexibility re- quired by cross-linguistic differences. On the other hand, the linguistic, unification-based processing involved in creating them can be carried out effi- ciently and without the need to reason about the domain or context; the QLF language has con- structs for explicit representation of contextually sensitive aspects of interpretation. When it is necessary, for correct translation, to resolve an ambiguity present at QLF level, the BCI system interacts with the source language user to make the necessary decision, asking for a choice between word sense paraphrases or between alter- native partial bracketings of the sentence. There • is thus a strong connection between our choice of a representation sensitive to context and the use of interaction to resolve context dependent ambi- guities, but in this paper we concentrate on repre- sentational and transfer issues. 2 CLE REPRESENTATION LEVELS In this section we explain how QLF fits into the overall architecture of the CLE and in section 3 we discuss the reasons for choosing it for interactive dialogue translation. 161 2.1 CLE Processing Phases A coarse view of the CLE architecture is that it consists of a linguistic analysis phase followed by a contextual interpretation phase. The output of the first phase is a set of alternative QLF analy- ses of a sentence, while the output of the second is an RQLF (resolved QLF) representation of the interpretation of an utterance: Sentence linguistic analysis ~ QL Fs Q X, Fs contextual interpretation *" R Q L F. Deriving a fairly conventional Logical Form (LF) from the RQLF is then a simple formal mapping which removes the information in the RQLF that is not concerned with truth conditions. Linguistic analysis and contextual interpreta- tion each consist of several subphases. For anal- ysis these are: orthography, morphological anal- ysis, syntactic analysis (parsing), and (composi- tional) semantic analysis. Apart from the first, these analysis subphases are based on the unifica- tion grammar paradigm, and they all use declara- tive bidirectional rules. When the CLE is being used as an interface to a computerized information system (e.g. a database system), its purpose is to derive an LF represen- tation giving the truth conditions of an utterance input by a user. The LF language is based on first order predicate logic extended with general- ized quantifiers and some other higher order con- structs (Alshawi and van Eijck, 1989). For ex- ample, in a context where she can refer to Mary Smith, and one to "a car", a possible LF for She hired one is: quant (exists ,C, [carl ,C], quant (exists ,E, [event ,El, [past, [hir • I, E, mary_smith, C] ] ) ). This can be paraphrased as "There is a car C, and an event E such that, in the past, ~. is a hiring event by Mary Smith of e." In this notation, quan- tified formulae consist of a generalized quantifier, a variable, a restriction and a scope; square brack- ets are used for the application of predicates and operators to their arguments. To arrive at such LF representations, a number of intermediate lev- els of representation are produced by successive modular components. Generation of linguistic expressions in the CLE takes place from QLFs (or from RQLFs by map- ping them to suitable QLFs). Since the rules used during the analysis phase are declarative and bidirectional, these are also used for generation. To achieve computationally efficient analysis and generation, the rules are pre-compiled in different ways for application in the two directions. Gen- eration uses the semantic-head driven algorithm (Shieber et al, 1990). 2.2 The QLF Language The QLF representations produced for a sen- tence are neutral with respect to the choice of ref- erents for pronouns and definite descriptions, and relations implied by compound nouns and ellip- sis. They are also neutral with respect to other ambiguities corresponding to alternative scopings of quantifiers and operators and to the collec- tive/distributive and referential/attributive dis- tinctions. The QLF is thus the level of represen- tation encoding the results of compositional lin- guistic analysis independently of contextually sen- sitive aspects of understanding. These aspects are addressed by the contextual interpretation phase which has the following subphases: quan- tifier scoping (Moran 1988), reference resolution (Alshawi 1990), and plausibility judgement. The QLF language is a superset of the LF language containing additional expressions corre- sponding, for example, to unresolved anaphors. More specifically, there are two additional term constructs (anaphoric terms and quanti- fied terms), and one additional formula construct (anaphoric formulae): a_term( Category, Entity Vat, Restriction). q_term( Category, Entity Vat, Restriction). a_form(Category, Pred Var , Restriction). These QLF constructs contain syntactic and morphological information in the Category and logical (truth-conditional) information in the Restriction, itself a QLF formula binding the vari- able. A QLF from which the LF for She hired one could have been derived is: [past, [hire, q_term (<t =quant, n=s ing>, E, [event, E] ), a_term(<t =ref, p=pro, l=she, n=sing>, Y, [female, Y] ), q_t erm (<t =quant, n=sing>, C, a_f orm(<t =pred, l=one>, P, [P.C]))]]. 162 in which categories are shown as lists of feature- value specifications (the feature shown are t for QLF expression type, n for number, p for phrase type, and 1 for lexical information). The differ- ences between the QLF shown here and the LF shown earlier are that the quantified terms have been scoped, the anaphoric term for she has been resolved to Mary Smith, and the anaphoric NP restriction implicit in one has been resolved using the predicate car. The RQLF representation of an utterance in- cludes all the information from the QLF, together with the resolutions of QLF constructs made dur- ing the contextual interpretation phase. For ex- ample, the referent of an a_term is unified with the a_term variable. Some constraints on plausibility can be ap- plied at the QLF level before a full interpreta- tion has been derived. This is because most of the predicate-argument structure of an utterance has been determined at that point, allowing, in particular, the application of sortal constraints expected by predicates of their arguments. Sor- tal constraints cut down on structural (e.g. at- tachment) ambiguity, and on word sense ambigu- ity, the latter being particularly important for the translation application in the context of large vo- cabularies. 3 REPRESENTATION LEVELS FOR TRANSFER The representational structures on which trans- fer operates must contain information correspond- ing to several linguistic levels, including syntax and semantics. For transfer to be general, it must operate recursively on input representations. We call the level of representation on which this re- cursion operates the "organizing" level; semantic structure is the natural choice, since the basic re- quirement of translation is that it preserves mean- ing. Syntactic phrase structure transfer, or deep- syntax transfer (e.g. Thurmair 1990, Nagao and Tsujii 1986) results in complex transfer rules, and the predicate-argument structure which is re- quired for the application of sortal restrictions is not represented. McCord's (1988, 1989) organizing level appears to be/hat, of surface syntax, with additional deep syntactic and semantic content attached to nodes. As we have argued, this level is not optimal, which may be related to the fact that McCord's sys- tem is explicitly not symmetrical: different gram- mars are used for the analysis and synthesis of the same language, which are viewed as quite differ- ent tasks. Isabelle and Macklovitch (1986) argue against such asymmetry between analysis and syn- thesis on the grounds that, although it is tempting as a short-cut to building a structure sufficiently well-specified for synthesis to take place, asym- metry means that the transfer component must contain a lot of knowledge about the target lan- guage, with dire consequences for the modularity of the system and the reusability of different parts of it. In the BCI, however, the transfer rules con- tain only cross-linguistic knowledge, allowing the analysis and generation to make use of exactly the same data. Kaplan et al (1989) allow multiple levels of representation to take part in the transfer rela- tion. However, Sadler et al (1990) point out that the particular approach to realizing this taken by Kaplan et al has problems of its own and does not cleanly separate monolingual from contrastive knowledge. The CLE processing subphases offer three se- mantic representations of different depth as can- didates for an appropriate transfer level, namely QLF, RQLF and LF. At the LF level, sortal re- strictions can be applied, but the form of noun phrase descriptions used and also information on topicalization is no longer present; the LF rep- resentation is too abstract for transfer. On the other hand, not all the information appearing in the RQLF about how QLF constructs have been resolved is necessary for translation. Resolved ref- erents are not an adequate generator input for def- inite descriptions in the target language, since the view of the referent in the source is lost during translation. Another case is that translation from resolved ellipsis can result in unwieldy target sen- tences. In arguing for QLF-level transfer, we are asserting that predicate-argument relations of the type used in QLF are the appropriate organizing level for compositional transfer, while not denying the need for syntactic information to ensure that, for example, topichood or the given/new distinc- tion is preserved. Finally, in contrast to systems such as Rosetta (Landsbergen, 1986) which depend on stating rule by rule correspondences between source and target grammars, we wish to make the monolingual de- scriptions as independent as possible from the task of translating between two languages. Apart from 163 its attractions from a theoretical point of view, this has practical advantages in allowing gram- mars to be reused for different language pairs and for applications other than translation. 4 QLF TRANSFER QLF transfer involves taking a QLF analysis of a source sentence, say QLF1, and deriving from it another expression, QLF2, from which it is possi- ble to generate a sentence in the target language. Leaving aside unresolved referential expressions, the main difference between QLF1 and QLF2 is that they will contain constants, particularly pred- icate constants, that originate in word sense en- tries from the lexicons of the respective languages. If more than one candidate source language QLF exists, the appropriate one is selected by present- ing the user with choices of word sense paraphrases and of bracketings relating to differences in the syntactic analyses from which the QLFs were de- rived. A transfer rule specifies a pair of QLF patterns. The left hand side matches QLF expressions for one language and the right hand side matches those for the other: trans(<QLFl subexpression pattern> <Operator> <QLF2 subexpression pattern>). If the operator is == then the rule is bidirectional. Otherwise, a single direction of applicability is in- dicated by use of one of the operators >= or =<. Transfer rules are applied recursively, this pro- cess following the recursive structure of the source QLF. In order to allow transfer between struc- turally different QLFs, rules with 'transfer vari- ables' need to be used. These variables, which take the form tr(atom), show how subexpressions in the source QLF correspond to subexpressions translating them in the target QLF. For exam- ple, the following rule expresses an equivalence between the English to be called ("I am called John"), and the Swedish beta ("Jag heter John"). trans ( [call_name, tr(ev), q_term(<tfquant ,n=sing>, A, [entity,A] ), tr(ag), tr(name)] [heCal, Cr (ev), tr (ag), Cr (name) ] ). Transfer rules often correspond directly to inter- lingual meaning postulates: when the expressions in a transfer rule are formulae, the symbols ==, >=, and =< can be read as the logical operators < >, >, and < respectively. A rule like Crans ([and, [bafll ,X], [luckl ,X]] [otur I, x] ) translating between the English bad luck and the Swedish otur, can be interpreted in this way. We will now assess the method's strengths and weaknesses, as they have manifested themselves in practice. We will pay particular attention to the criteria of expressiveness, compositionality, sim- plicity, reversibility and monotonicity. We take the last point first, since it is the most straightforward one. Since rules are applied purely nondeterministically and by pure unification, we get monotonicity "for free" - although there is a case for disallowing transfer by decomposition of a complex QLF structure which directly matches one side of a transfer rule. The other points need more discussion. 4.1 Expressiveness Since we are intentionally limiting ourselves by not allowing access to full syntactic information (but only to that placed in QLF categories) in the transfer phase, it is legitimate to wonder whether the formalism can really be sufficiently expressive. Here, we will attempt to answer this criticism; we begin by noting that shortcomings in this area can be of several distinct kinds. Sometimes, a formal- ism can appear to make it necessary to write many rules, where one feels intuitively that one should be enough; we treat this kind of problem under the heading of compositionality. In other cases, the difficulty is rather that there does not appear to be any way of expressing the rule at all in terms of the given formalism. In our case, a fair proportion of problems that at first seem to fall into this cate- gory can be eliminated by having adequate mono- lingual grammars and using the target grammar as a filter; the idea is to allow the transfer com- ponent to produce unacceptable QLFs which are filtered out by fully constrained target grammars. A good example of the use of this technique is the English definite article, which in Swedish can be translated as a gender-dependent article, but preferably is omitted; however, an article is oblig- atory before an adjective. Solving this problem 164 [, Table 1: Types of complex transfer used Type Example Different John likes Mary particles John tyeker om Mary Passive Insurance is included to active FSrs~ikring ingAr Verb John owes Mary $20 to adjective John ~ir skyldig Mary $20 Support verb John had an accident to normal verb John rltkade ut fdr en olycka Single verb to phrase Idiomatic use of PP John wants a car John vii1 ha en bil (lit.: "wants to have") John is in a hurry John har br•ttom (lit.: "has hurry") at transfer level is not possible, since the transfer component has no way of knowing that a piece of logical form will be realized as an adjective; there are many cases where an adjective-noun combina- tion in English is best translated as a compound noun in Swedish. Exploiting the fact that the rele- vant constraint is present in the Swedish grammar, however, the "transfer-and-filter" method reduces the problem to two simple lexical rules. Sortal re- strictions at the target end can also be used as a filter in a similar way. 4.2 Simplicity and reversibility The most obvious way to put the case with re- gard to simplicity is by giving a count of the vari- ous categories of rule, and providing evidence that there is a substantial proportion of rules which are simple in our framework, but would not necessar- ily be so in others. The transfer component currently contains 718 rules. 576 of these (80.2%) have the property that both the right- and left-hand sides are atomic. 502 members of this first group (69.9%) translate senses of single words to senses of single words; the remaining 74 (10.3%) translate atomic con- stants representing the senses of complex syntactic constructions, most commonly verbs taking parti- cles, reflexives, or complementizers. An example is the following rule, which defines an equivalence between English care about ('John cares about Mary") and Swedish bry sig om ( "John bryr sig om Mary", lit. "John cares himself about Mary"). J Table ~: Transfer contexts used 'context Example ' Perfect tense Negated John has liked Mary John har tyckt om Mary John doesn't like Mary John tycker inte om Mary YN-question Does John like Mary? Tycker John om Mary? WH-question Who does John like? Veto tycker John om? Passive Mary was liked by John Mary blev omtyckt av John Relative The woman that John likes clause Sentential complement Embedded question VP modifier Object raising Change of aspect Kvinnan som John tycker om I think John likes Mary Jag tror John tycker om Mary I know who John likes Jag vet vem John tycker om John likes Mary today John tycker om Mary idag I want John to like Mary Jag vill att John ska tycka om Mary ("I want that J. shall like M.") John stopped liking Mary John slutade tycka om Mary ("J. stopped like-INF M.") trans(care_about == bry_sig_om). Since vocabulary has primarily been selected with regard to utility (we have, for example, made considerable use of frequency dictionaries (Alldn 1970)), we think it reasonable to claim that QLF- based transfer is simplifying the construction of transfer rules in a substantial proportion of the commonly encountered cases. On the score of reversibility, we will once again count cases; here we find that 659 (91.8%) of the rules are reversible, 17 (2.4%) work only in the English-Swedish direction, and 42 (5.8%) only in the Swedish-English direction. These also seem to be fairly good figures. 4.3 Compositionality As in any rule-based system, "compositionality" corresponds to the extent to which it is necessary to provide special mechanisms to cover cases of ir- regular interactions between rules. As far as we know, there is no accepted benchmark for testing 165 compositionality of transfer; what we have done, as a first step in this direction, is to select six com- mon types of complex transfer, and eleven com- mon contexts in which they can occur. These are summarized in tables 1 and 2 respectively. Each complex transfer type is represented by a sample rule, as shown in table 1; the question is the ex- tent to which the complex transfer rules continue to function in the different contexts (table 2). To test transfer compositionality properly, it is not sufficient simply to note which rule/context combinations are handled correctly; after all, it is always possible to create a completely ad hoc so- lution by simply adding one transfer rule for each combination. The problem must rather be posed in the following terms: if there is a single rule for each complex transfer type, and a number of rules for each context, how many extra rules must be added to cover special combinations? It is this issue we will address. The actual results of the tests were as follows. There were 124 meaningful combinations (some constructions could not be passivized); in 103 of these, transfer was perfectly compositional, and no extra rule was needed. For example, the English sentence for the combination "Verb to adjective + WH-question" is How much does John owe Mary. The corresponding Swedish sentence is Hut my- cket dr John skyldig Mary? ("How much is John indebted-to Mary?"), and the two QLFs areS: [uhq, [pres, [owe_have_to_pay, q_term(<t=quant,n=sing>,A,[event,A]), a_term(<t=ref,p=name>, B,[name_of,B,john]), q_term(<t=quant,l=wh>,C,[quantity,C]), a_term(<t=ref,p=name>, D,[name_of,D,mary])]]] [whq, [pro8 ent, [Va.T a, q_t erm(<t =quanE, n=sing>, A, [state. A] ), [skyldiE_nsn_nst, a_t erm (<t =ref, p=name>, B, [name_o~, B, j ohn3 ), a_term(<t=ref, p=name>, C, [name_of, C,mary] ), q_t erm(<t =quant, l=wh> ,D, [quantity, D] )] ] ] ] It should be evident that the complex transfer rule defining the equivalence between owe and yarn skyldig, transC[owe_have_to_pay, q_termC<t=quant,n=sing>,A,[event,A]), tr(ag),tr(sum),tr(obj)] [vara, q_term(<t=quant,n=sing>,A,[state,A]), [skyldig_ngn_ngt, trCag),trCobj),tr(sum)]]). is quite unaffected by being used in the context of a Wit-question. Of the remaining 21 rule/context/direction triples, seven failed for basically uninteresting rea- sons: the combination "Perfect tense + Passive- to-active" did not generate in English, and the six sentences with the object-raising rule all failed in the Swedish-English direction due to the transfer component's current inability to create a function- application from a closed form. The final fourteen failures are significant from our point of view, and it is interesting to note that all of them resulted from mismatches in the scope of tense and nega- tion operators. The question now becomes that of ascertaining the generality of the extra rules that need to be added to solve these fourteen unwanted interac- tions. Analysis showed that it was possible to add 26 extra rules (two of which were relevant here), which reordered the scopes of tense, nega- tion and modifiers, and accounted for the scope differences between the English and Swedish QLFs arising from the general divergences in word-order and negation of main verbs. These solved ten of the outstanding cases. For example, the com- bination "Different particles + Negated" is John doesn't like Mary in English and John tycker inte om Mary (lit.: "John thinks not about Mary") in Swedish; the QLF-pair is: [pres p [not, [like, q_t erm(<t=quant ,n=sing>, A, [event, A] ), a_term ( <t=ref, p=name>, B, [name_o~, B, j ohn] ), a_termC<t=ref, p=name>, B, [~ame_of, B ,mary] )] ] ] 2 ~r is the present tense of ~ara. 166 [not, [present, [tycka_om, q_t erm(<t =quant, n=s ing>, A, [event, A] ), a_t erm(<t =ref, p=name>, B, [name_of, B, john] ), a_term(<t=ref, p=name>, B, [name_o:f, B, mary] ) ] ] ] The extra rule here, trans( [pres, [not,tr(body)]] == [not, [present, tr (body)] ] ). reorders the scopes of the negation and present- tense operators, but does not need to access the interior structure of the QLF (the "body" vari- able); this turns out to be the case for most inter- actions of negation, VP-modification and complex transfer. It is thus not surprising that a small number of similar rules covers most of the cases. The four bad interactions left all involved the English verb to be; these were the combinations "Passive to active ÷ VP modifier" and "Idiomatic use of PP q- negation", which failed to transfer in either direction. Here, there is no general solu- tion involving the addition of a small number of extra rules, since the problem is caused by an oc- currence of to be on the English side that is not matched by an occurrence of the corresponding Swedish word on the other. The solution must rather be to add an extra rule for each complex fransfer rule in the relevan~ class to cover the bad interaction. To solve the specific examples in the test set, two extra rules were thus required. Summarizing the picture, the tests revealed that all bad interactions between the transfer rules and contexts shown here could be removed by adding four extra rules to cover the 124 possible interac- tions. In a general perspective (viewing the rules as representatives of their respective classes), the rule-interaction problems exemplified by the con- crete collisions were solved by adding • 26 general rules to cover certain standard scope mismatches caused by verb-inversion and negation. • two extra rules (one for present and one for past tense) for each complex transfer rule of either the "Idiomatic use of PP" or "Active to Passive" types, to cover idiosyncratic in- teractions of these with negation and VP- modification respectively. We view these results as very promising: there were few bad interactions, and those that ex- isted were of a regular nature that could be coun- teracted without fear of further unwelcome side- effects. This gives good grounds for hoping that the system could be scaled up to a practically use- ful size without suffering the usual fate of drown- ing in a sea of ad hoc fixes. 5 IMPLEMENTATION STATUS The current implementation includes analysis, transfer, and generation modules, sizable gram- mars with morphological, syntactic and semantic rules for English and Swedish, and an experimen- tal set of transfer rules for this language pair. Rel- ative to the size of the grammars, the lexicons are still small (approximately 2000 and 1000 words re- spectively). About 250 entries for each language have been added for a specific domain (car hire), which makes possible moderately unconstrained conversation on this topic; the system, including the facilities for interactive resolution of trans- lation problems, has been tested on a corpus of about 400 sentences relating to the domain. For short sentences typical of the car hire domain, me- dian total processing times for analysis, transfer and generation are around ten seconds when run- ning under Quintus Prolog on a SUN SPARCst~- tion 2. We are currently investigating a different QLF representation of Iense, aspect and modality which should increase the transfer compositionality for the operator cases we have discussed in this pa- per, as well as allowing more flexible resolution of temporal relations in applications other than translation. ACKNOWLEDGMENTS The work reported here was funded by the Swedish Institute of Computer Science, and the greater part of it was carried out while the third author was employed there. We would like to thank Steve Pulman for many helpful discussions, especially with regard to the problems encoun- tered in adapting the English grammar to Swedish. 167 REFERENCES Alldn, Sture (ed.) (1970) Frequency Dictionary of Present-Day Swedish, Almqvist & Wiksell, Stockholm. 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Trans- fer takes place at the level of Quasi Logical Form (QLF), a contextually sensitive logical form rep- resentation which is deep enough for. syntactic and morphological information in the Category and logical (truth-conditional) information in the Restriction, itself a QLF formula binding the